feat(py): Implemented xai plugin

[zarinn3pal]

Implemented xAI plugin with tests and samples

[gemini-code-assist]

Summary of Changes

Hello @zarinn3pal, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request delivers a significant new feature by implementing a Python plugin for Genkit, allowing seamless integration with xAI's Grok large language models. The plugin provides robust capabilities for generating text, handling streaming responses, and utilizing tools, thereby expanding Genkit's ecosystem with another powerful AI provider. The inclusion of tests and a practical sample application ensures that developers can easily adopt and experiment with xAI models in their Genkit projects.

Highlights

• New xAI Plugin: Introduced a new Python plugin for Genkit that integrates with xAI's Grok models, enabling interaction with various Grok 3 and Grok 2 Vision models.
• Model Capabilities: The plugin supports both synchronous and streaming text generation, as well as advanced features like tool calling, system roles, and custom generation configurations.
• Comprehensive Testing: Includes a dedicated test suite for the xAI models and plugin, ensuring functionality for basic generation, streaming, tool handling, and configuration parsing.
• Sample Application: A new sample application (xai-hello) has been added to demonstrate the plugin's usage, showcasing simple text generation, streaming, and tool integration (weather and calculator functions).
• Project Integration: The main pyproject.toml has been updated to include the new genkit-plugin-xai as a dependency and a workspace member, making it discoverable within the project.

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[gemini-code-assist]

Code Review

This pull request introduces a new Genkit plugin for xAI models. The implementation is comprehensive, covering model definition, generation (both standard and streaming), tool support, and configuration. The code is well-structured and includes a good set of unit tests and a sample application.

I've identified a couple of high-severity issues in the model implementation (models.py) related to handling tool schemas and media parts, which could lead to runtime errors. My review includes specific suggestions to fix these. Overall, this is a great addition to the Genkit ecosystem.